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Author Naveen Onkarappa; Angel Sappa edit  doi
isbn  openurl
  Title On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow Type Conference Article
  Year 2010 Publication 7th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume 6111 Issue Pages 230-239  
  Keywords  
  Abstract This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach.  
  Address Povoa de Varzim (Portugal)  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-13771-6 Medium  
  Area Expedition Conference ICIAR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ OnS2010 Serial 1342  
Permanent link to this record
 
 
Author Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie edit   pdf
url  openurl
  Title Inferring the Performance of Medical Imaging Algorithms Type Conference Article
  Year 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6854 Issue Pages 520-528  
  Keywords Validation, Statistical Inference, Medical Imaging Algorithms.  
  Abstract Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.		  
  Address Sevilla  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Heidelberg Place of Publication Berlin Editor Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch  
  Language Summary Language Original Title  
  Series Editor Series Title L Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CAIP  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HGR2011 Serial 1676  
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Author Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados edit  doi
isbn  openurl
  Title Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval Type Conference Article
  Year 2011 Publication 33rd European Conference on Information Retrieval Abbreviated Journal  
  Volume 6611 Issue Pages 314-325  
  Keywords  
  Abstract In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.  
  Address Dublin, Ireland  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Berlin Editor P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-642-20160-8 Medium  
  Area Expedition Conference ECIR  
  Notes DAG; RV;ADAS Approved no  
  Call Number Admin @ si @ RAK2011 Serial 1737  
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Author Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers edit   pdf
doi  isbn
openurl 
  Title Improving HOG with Image Segmentation: Application to Human Detection Type Conference Article
  Year 2012 Publication 11th International Conference on Advanced Concepts for Intelligent Vision Systems Abbreviated Journal  
  Volume 7517 Issue Pages 178-189  
  Keywords Segmentation; Pedestrian Detection  
  Abstract In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.		  
  Address Brno, Czech Republic  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor J. Blanc-Talon et al.  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-33139-8 Medium  
  Area Expedition Conference ACIVS  
  Notes ADAS;ISE Approved no  
  Call Number ADAS @ adas @ SLV2012 Serial 1980  
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Author Patricia Marquez;Debora Gil;Aura Hernandez-Sabate edit   pdf
doi  isbn
openurl 
  Title A Complete Confidence Framework for Optical Flow Type Conference Article
  Year 2012 Publication 12th European Conference on Computer Vision – Workshops and Demonstrations Abbreviated Journal  
  Volume 7584 Issue 2 Pages 124-133  
  Keywords Optical flow, confidence measures, sparsification plots, error prediction plots  
  Abstract Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Florence, Italy, October 7-13, 2012 Editor Andrea Fusiello, Vittorio Murino ,Rita Cucchiara  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-642-33867-0 Medium  
  Area Expedition Conference ECCVW  
  Notes IAM;ADAS; Approved no  
  Call Number IAM @ iam @ MGH2012b Serial 1991  
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Author Fernando Barrera; Felipe Lumbreras; Angel Sappa edit   pdf
doi  isbn
openurl 
  Title Evaluation of Similarity Functions in Multimodal Stereo Type Conference Article
  Year 2012 Publication 9th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume 7324 Issue I Pages 320-329  
  Keywords Aveiro, Portugal  
  Abstract This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-31294-6 Medium  
  Area Expedition Conference ICIAR  
  Notes ADAS Approved no  
  Call Number BLS2012a Serial 2014  
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Author Miguel Oliveira; Angel Sappa; V. Santos edit   pdf
doi  isbn
openurl 
  Title Color Correction using 3D Gaussian Mixture Models Type Conference Article
  Year 2012 Publication 9th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume 7324 Issue I Pages 97-106  
  Keywords  
  Abstract The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 10.1007/978-3-642-31295-3_12 Medium  
  Area Expedition Conference ICIAR  
  Notes ADAS Approved no  
  Call Number Admin @ si @ OSS2012a Serial 2015  
Permanent link to this record
 
 
Author Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Road Scene Segmentation from a Single Image Type Conference Article
  Year 2012 Publication 12th European Conference on Computer Vision Abbreviated Journal  
  Volume 7578 Issue VII Pages 376-389  
  Keywords road detection  
  Abstract Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images.
From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined		  
  Address Florence, Italy  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-33785-7 Medium  
  Area Expedition Conference ECCV  
  Notes ADAS;ISE Approved no  
  Call Number Admin @ si @ AGL2012; ADAS @ adas @ agl2012a Serial 2022  
Permanent link to this record
 
 
Author Naveen Onkarappa; Angel Sappa edit  doi
isbn  openurl
  Title Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario Type Conference Article
  Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 8048 Issue Pages 483-490  
  Keywords Optical flow; regularization; Driver Assistance Systems; Performance Evaluation  
  Abstract Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).  
  Address York; UK; August 2013  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-40245-6 Medium  
  Area Expedition Conference CAIP  
  Notes ADAS; 600.055; 601.215 Approved no  
  Call Number Admin @ si @ OnS2013b Serial 2244  
Permanent link to this record
 
 
Author Karel Paleček; David Geronimo; Frederic Lerasle edit  doi
isbn  openurl
  Title Pre-attention cues for person detection Type Conference Article
  Year 2012 Publication Cognitive Behavioural Systems, COST 2102 International Training School Abbreviated Journal  
  Volume Issue Pages 225-235  
  Keywords  
  Abstract Current state-of-the-art person detectors have been proven reliable and achieve very good detection rates. However, the performance is often far from real time, which limits their use to low resolution images only. In this paper, we deal with candidate window generation problem for person detection, i.e. we want to reduce the computational complexity of a person detector by reducing the number of regions that has to be evaluated. We base our work on Alexe’s paper [1], which introduced several pre-attention cues for generic object detection. We evaluate these cues in the context of person detection and show that their performance degrades rapidly for scenes containing multiple objects of interest such as pictures from urban environment. We extend this set by new cues, which better suits our class-specific task. The cues are designed to be simple and efficient, so that they can be used in the pre-attention phase of a more complex sliding window based person detector.  
  Address Dresden, Germany  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (down) LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-34583-8 Medium  
  Area Expedition Conference COST-TS  
  Notes ADAS Approved no  
  Call Number Admin @ si @ PGL2012 Serial 2148  
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